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The most useful AI bug report is a deterministic handoff: identify the exact product, model and build, pin the repository and runtime, state expected behavior before observed behavior, and provide a minimal copy-pasteable reproducer. Include complete errors and sanitized evidence, describe frequency and nondeterminism, explain impact and boundaries, then propose an invariant and regression test without presenting an unproven cause as fact.

What makes an AI bug report reproducible

Developers should be able to start from a clean environment and reach the same failure using only your report and its attached fixture. Microsoft’s VCSamples guidance describes a good repro as minimal—“as small and as simple as possible while still demonstrating the issue.” Minimal does not mean vague. It means every included line, dependency and setting is necessary, while every omitted detail is genuinely irrelevant.

A report is fixable when it answers five questions in order:

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  1. What exact software, model, code and environment were involved?
  2. What should have happened, stated as a testable invariant?
  3. What happened instead, with the complete output?
  4. How can another developer trigger it safely and repeatedly?
  5. What impact and regression test define a successful fix?

Separate evidence from interpretation. A plausible explanation, a recent code change, or a successful run on another release does not establish root cause. Ask maintainers to confirm the cause after they can reproduce the behavior.

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Capture identity and scope before changing anything

Record the failing state while it is still available. AI systems can change behavior when a model alias moves, a tool is upgraded, a prompt is edited, or a hosted service rolls out a new deployment. Use UTC for the time window so people in different regions can correlate events.

Identity to record What to include Why it matters
AI product and model Product name, exact model identifier (not only a family name), tool or agent version, build number and feature flags Aliases and orchestration layers can route to different behavior.
Code and release Repository URL, commit SHA, branch, package release, deployment identifier and the first known failing version A maintainer needs to check the same source and binary.
Runtime Operating system and architecture, language and version, compiler or interpreter version, container or image digest, GPU or accelerator details when relevant Native libraries, tokenizers and drivers can alter results.
Dependencies Lockfile name and checksum, package-manager version, model or tokenizer files and hashes Unpinned transitive packages are a common source of drift.
Configuration Environment variables with secrets removed, endpoint or region, permissions, tool schemas, retrieval settings, seed, temperature and sampling limits Settings define the execution path and whether output is stochastic.
Time and scope UTC start and end, affected users or jobs, deployment region, frequency and versions tested without the failure Operations teams can correlate traces and establish boundaries.

Keep a copy of the exact request, prompt, tool-call payload, retrieved context and response. For a production incident, also record release identity, correlated log and trace IDs, repository guidance, a safe reproduction fixture and the test baseline. An incident summary should state the UTC window, expected and observed behavior, impact and the releases that were tested.

Redact safely, not selectively

Remove API keys, session cookies, OAuth tokens, signing material, database credentials, internal hostnames when they are sensitive, customer records and personal data. Replace each secret with a stable marker such as REDACTED_API_KEY_1, and explain its type and length if that affects parsing. Do not replace an entire request with “credentials removed” when the authentication header, field order or token shape is part of the failure; provide a synthetic value with the same structural properties.

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Review prompts and retrieved documents for hidden instructions, email addresses, account numbers and proprietary text. Preserve the smallest safe fixture that exercises the path without contacting a live service. SARIF guidance for AI findings likewise calls for structured reproduction evidence while redacting credentials, tokens and personally identifiable information.

State expected behavior before observed behavior

Write expected behavior as one testable statement, including the invariant that must hold. “The assistant should work” is not testable. “Given the request fixture and a read-only tool, the orchestrator must not mutate the repository and must return a validation error when the tool schema is invalid” is testable.

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Then describe the actual behavior exactly:

  • Copy the complete error message, HTTP status and response body, or full traceback. Preserve capitalization, punctuation and line breaks.
  • State whether the process crashed, hung, timed out, returned an incorrect answer, made an incorrect tool call, changed state, violated permissions or truncated output.
  • Give the observed frequency, such as 10 of 10 runs, 3 of 20 runs, or only once in a particular deployment.
  • Include a screenshot only when visual layout or a missing UI element cannot be represented as text.

Do not paraphrase an error and then omit the original. A developer cannot search logs or write an assertion against a paraphrase.

Build a minimal, self-contained reproducer

Start with the smallest input that still fails, then remove one element at a time. Microsoft’s repro guidance requires all code, exact commands, compiler version and necessary setup, while removing unrelated dependencies. scikit-learn similarly advises reading a project’s issue template and providing code that includes every import and data-retrieval step so it can be copied and run.

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Reduce the fixture systematically

  1. Copy the failing command, request or test into a new directory.
  2. Replace production data with a tiny synthetic fixture that preserves the triggering shape: for example, two records with the same field collision, a short document that exceeds the relevant token boundary, or a tool schema containing the offending type.
  3. Delete files, libraries, middleware and prompt sections one at a time. Run after each deletion.
  4. Freeze the last version that still fails and record the change that made it stop failing. Do not assume that change is the cause; it only defines a useful boundary.
  5. Package the remaining files, lockfile and setup instructions so a fresh developer can run them without access to your network or credentials.

Preserve AI inputs as data

Include the exact prompt or message array, system instructions, tool definitions, retrieved passages, model settings and generated tool arguments. Keep role labels, whitespace and encoding when they influence tokenization or parsing. If the output is stochastic, save several request/response pairs and the seed or sampling settings when the API exposes them.

Distinguish a bad model answer from a product defect. A wrong answer may be model quality. An incorrect function argument, unauthorized state mutation, permission failure, truncation, timeout or orchestration regression is a software behavior that can often be tested deterministically. State which category the evidence currently supports without claiming more.

Give numbered reproduction steps with checkpoints

Write commands from a clean checkout, not actions you performed from memory. Every step should name its precondition and the result a developer should see before continuing.

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  1. Clone the repository and check out the exact commit SHA.
  2. Install the stated runtime and dependency lock with the project’s documented package-manager command.
  3. Create the synthetic fixture and apply only the configuration listed in the report. Use environment variables that contain safe, non-production values.
  4. Run the exact command, request fixture or test command. Include flags, working directory and input file paths.
  5. Compare the output with the expected checkpoint. Save stdout, stderr, exit status, response headers and body, and trace or request IDs.
  6. Repeat the run the number of times needed to establish frequency. If the failure is intermittent, record every result rather than only the failing one.

Use a shell transcript or a script when possible. For example, a safe verification command might be python -m pytest tests/test_tool_schema.py -q; replace it with the project’s real command and state the expected pass or failure. Never ask a maintainer to run a command that sends real customer data or mutates production.

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Attach complete, correlated evidence

Evidence should let a maintainer connect the local reproducer to the service behavior:

  • Sanitized logs covering the failure and the preceding setup, with timestamps in UTC.
  • Trace IDs, request IDs, job IDs and relevant file paths and line numbers.
  • The prompt, request, response and tool-call fixtures in their original structured format.
  • Model and tool settings, token counts or truncation indicators when available.
  • A baseline run from the last known good release, if one exists, using the same fixture.
  • Checksums for large fixtures, model files or container images so downloads can be verified.

Keep evidence in text or machine-readable files rather than a screenshot of a terminal. If logs contain secrets, sanitize them consistently and explain what was replaced. Correlation IDs are more useful than a large unfiltered log dump.

Measure determinism instead of guessing

Run the same fixture repeatedly under unchanged conditions. Record the number of successes and failures, the seed, temperature, top-p or equivalent sampling settings, and the exact model/version identifier. A result that fails 10 of 10 times with a fixed seed is different from a result that fails 2 of 20 times with sampling enabled.

Test one variable at a time: the last known good model, the previous orchestration release, a different runtime, or a clean dependency install. Report each result in a small matrix rather than saying that an upgrade “seems to fix it.” A successful test on another release narrows the boundary; it does not prove which change caused the regression.

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Run dimension Record
Repeatability Attempts, failures, pass/fail sequence and whether a fresh process changes the result
Sampling Seed, temperature, top-p, candidate limits and whether deterministic mode was enabled
Version comparison Exact model, tool, repository and dependency versions for each run
Environment comparison OS, runtime, architecture, container image and relevant permissions

Explain impact and boundaries

Describe who or what is affected and what happens if the issue is not fixed. Include severity, data exposure, lost work, incorrect decisions, availability impact and any safe workaround. State whether the problem affects all users or only a region, model, permission set, input size or deployment.

List the first version that fails and versions tested that do not fail. If a workaround changes behavior, document its compatibility and cost. Do not call a workaround a fix until the invariant has been verified.

Make the report useful to the fixer

End with a concise handoff:

  • Suspected invariant: the rule that should remain true, such as “a read-only tool call never writes to the repository.” Label this as a hypothesis if it has not been proven.
  • Regression test: the smallest automated test that fails before the change and passes after it, using the safe fixture.
  • Compatibility constraints: supported runtimes, model versions, latency limits, permissions and backward-compatibility requirements.
  • Verification command: the exact command a maintainer can run after the change.

Microsoft Security notes that a reproducible trigger strengthens a report, guides the fix and can seed a regression test. The goal is not to prescribe an implementation; it is to make the required behavior executable.

A complete AI bug-report template

Title: [AI tool/model] [unexpected result] in [component]

Scope and identity
- Product and model:
- Tool/build and feature flags:
- Repository and commit SHA:
- Deployment/release:
- OS and architecture:
- Runtime/compiler:
- Dependency lock and checksum:
- Configuration (secrets removed):
- UTC window:

Expected behavior
- Invariant:
- Testable expected result:

Actual behavior
- Exact output/error/traceback:
- Exit status, crash, hang or timeout:
- Frequency and deterministic settings:

Minimal reproducer
- Files and fixture description:
- Exact setup commands:
- Exact run command:

Steps to reproduce
1. 
2. 
3. 

Evidence
- Sanitized logs:
- Trace/request IDs:
- Prompt, tool-call and response fixtures:
- Last known good baseline:

Impact and boundaries
- Affected users or systems:
- Severity and data exposure:
- First failing version:
- Versions tested without failure:
- Safe workaround:

Fix handoff
- Suspected violated invariant (hypothesis):
- Candidate regression test:
- Compatibility constraints:
- Verification command:

Replace each field with a concrete value or write “not observed” when you genuinely have no evidence. Do not leave a maintainer to infer whether an omitted field was checked.

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When maintainers cannot reproduce it

Check for hidden state

Compare lockfiles, environment variables, current working directory, file permissions, cache contents, model downloads, locale, timezone and network responses. A clean environment test should be stated explicitly, including which setup steps were verified.

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Separate service drift from code drift

Hosted models, retrieval indexes and policy configurations can change without a repository commit. Save the model identifier, endpoint or region, UTC timestamps and request IDs. If the service cannot provide a versioned snapshot, say that the behavior is time-bound and attach the captured fixture and response.

Turn intermittent failures into data

Increase the number of independent runs, preserve both passing and failing outputs, and report the rate with the exact settings. If the failure disappears when logging is enabled, note that observation; it may indicate a race or timeout, not that the bug is resolved.

How to judge a report or template

Whether you use an issue form, an incident system or a plain text file, evaluate it against the same five axes:

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Axis Pass condition
Reproducibility A fresh developer can run the fixture and reach the stated checkpoint.
Diagnostic completeness Exact output, context, logs and correlation identifiers are preserved.
Environment fidelity Versions, dependencies, model identity and configuration are pinned.
Privacy and safety Secrets, tokens, personal data and production side effects are excluded.
Fix readiness An invariant, compatibility boundary and regression-test command are defined.

A form that collects many fields but accepts “latest” for every version is less useful than a short form with exact identities and a runnable fixture.

Why this discipline improves fixes

Microsoft Security reported 155 internally validated reports across 23 open-source projects over three months in 2026; 93 reports across 14 projects or project families had documented maintainer acknowledgement or acceptance at publication time. Those figures do not guarantee that every report will be accepted, but they illustrate why structured, reproducible evidence is valuable to maintainers working across many projects.

The practical outcome is a shorter path from symptom to test: a maintainer can reproduce the trigger, identify the violated invariant, implement a narrow change, and retain the fixture as a regression test. If any link is missing, the report may still be worth filing, but its uncertainty should be explicit.

Final pre-submission checklist

  • Product, exact model, tool build, repository commit and deployment are identified.
  • OS, runtime, architecture, dependency lock and relevant configuration are pinned.
  • UTC time window, affected scope and first failing version are stated.
  • Expected behavior and invariant appear before observed behavior.
  • Complete error, traceback, response, exit status and frequency are included.
  • Prompt, tool calls, retrieved context and model settings are preserved in a safe fixture.
  • Secrets, credentials, tokens, customer data and PII are removed or replaced with stable synthetic values.
  • Numbered commands run from a clean environment and include expected checkpoints.
  • Logs, trace IDs and a last-known-good baseline are attached when available.
  • Impact, boundaries, workaround and compatibility constraints are described.
  • A suspected cause is labeled as a hypothesis, not asserted as fact.
  • A regression test and exact verification command are specified.

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